typos

README.md

@@ -6,7 +6,7 @@ tags:
 - token classification
 license: agpl-3.0
 datasets:
-- EMBO/sd-panels
+- EMBO/sd-nlp
 metrics:
 -
 ---
@@ -15,7 +15,7 @@ metrics:
 
 ## Model description
 
-This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It as then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `ROLES` task to perform pure context-dependent semantic role classification of bioentities.
+This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It as then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `GENEPROD_ROLS` configuration to perform pure context-dependent semantic role classification of bioentities.
 
 
 ## Intended uses & limitations
@@ -51,8 +51,10 @@ The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs.
 
 Training code is available at https://github.com/source-data/soda-roberta
 
+- Model fine-tuned: EMBL/bio-lm
 - Tokenizer vocab size: 50265
-- Training data: EMBO/biolang MLM
+- Training data: EMBO/sd-nlp
+- Dataset configuration: GENEPROD_ROLES
 - Training with 48771 examples.
 - Evaluating on 13801 examples.
 - Training on 15 features: O, I-CONTROLLED_VAR, B-CONTROLLED_VAR, I-MEASURED_VAR, B-MEASURED_VAR